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Thinking with Data

Thinking with Data

Learn with our expert faculty

facultyChristian Brownlees
PhD, University of Florence
UPF and BSE
Director
facultyMohammad Ghaderi
PhD, ESADE
UPF and BSE
Instructor

Course overview

More and more companies turn to big data and artificial intelligence to improve firm performance, but many initiatives produce dissapointing results. A key reason for failure is that companies do not fully appreciate the role of human intelligence. They focus too much on machines and their algorithms and not enough on humans and their intuitions. 

This 10-hour online course provides an introduction to data-driven, decision-making and decision-driven data analytics. 

Learn to skillfully interrogate data, understand the benefits, limits and threats of using and misusing data analytics 

During this course you will learn:

  • What distinguishes human from artificial intelligence
  • To question your intuitions, and develop a habit to start thinking with data
  • To recognize and avoid the most important cognitive biases that can interfere with good data-driven decision-making
  • When an explanation is essential for decision-making
  • Why simple statistical models are often desirable

Learn to work with and extract useful insights from real data

This course will be taught online, but it will be live and interactive: theory will be followed by in-class discussions and real-case analysis.

INTENSIVE COURSE

Thinking with Data

Applications will open soon!
  ONLINE
Regular Fee 950 €
Reduced Fee 550 €

10% early-bird discount applies to payments made on or before January 27, 2022 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Early-bird payment deadline: January 27, 2022

  ONLINE
Regular Fee 950 €
Reduced Fee 550 €

10% early-bird discount applies to payments made on or before January 27, 2022 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Last day to apply: February 22, 2022

  ONLINE
Regular Fee 950 €
Reduced Fee 550 €

10% early-bird discount applies to payments made on or before January 27, 2022 at 23:59 (CET)

See below for reduced fee eligibility

This edition is closed. Next edition TBA.

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Schedule

Times listed are Central European Time (CET). Compare with your time zone on time.is

Day / Time Day 1 Day 2 Day 3 Day 4 Day 5
14:30 - 16:30 Decision Making  Decisions with Data part I Decisions with Data part II Decisions with Data (inference) Case Study

Course content

Read an outline of topics and bibliography for this course

Course outline

1. Decisions, decisions, decisions! (focusing on human decision-making and cognitive biases)

Discussing elements of the human decision-making process and why they make our decisions prone to various types of mistakes when we need to make seemingly simple but crucial managerial decisions.

2. Decisions with data (structuring decision problem) Part I

Discussing situations in which we fail to incorporate, adequately, facts and statistical data into the managerial decision-making process.

3. Decisions with data (structuring decision problem) Part II

Through several examples and case discussions, we will practice a better way of thinking about thinking with data

4. Decisions with data (inference)

Discussing some useful statistical tools, focusing on their advantages and limitations, and learning which managerial decisions can benefit from them and in which circumstances we need to apply them cautiously.

5. Carter Racing case analysis, discussion and wrap up

Putting all the previously discussed elements into a real case discussion. You'll be provided with real data upon which you must make a decision in the challenging context of the case.

 

References

  • Gilboa, I. (2010). Making better decisions: Decision theory in practice. John Wiley & Sons.
  • Manski, C. F. (2013). Public policy in an uncertain world: analysis and decisions. Harvard University Press. [Chapter 1]
  • Frank, C. J., Magnone, P. F., & Netzer, O. (2022). Decisions Over Decimals: Striking the Balance between Intuition and Information. John Wiley & Sons.

Additional references

  • Bertsimas, D., & Freund, R. (2002). Data models et decisions. South Western College Publ.
  • Bazerman, M. H. (2006). Judgment in managerial decision making, John Wiley&Sons. Inc. Hoboken, NJ, USA

Prerequisites

Familiarity with Probability and Statistics (e.g., Bayes' theorem & basics of a regression model). Some basic familiarity with core concepts in decision theory (e.g., risk and uncertainty, utility theory, regret) would help but is not necessary.

Selected articles and case studies will be provided and you will be expected to have read them before each class.

Students must have their own laptop or desktop computer and good Internet connection to be able to follow and fully benefit from the course.

Participant Profile

Professionals, researchers and Master's or PhD students who want to learn how to use data effectively to get insights from large collections of data for their business or reaserch projects. 

In particular, this course may also be of interest to: 

  • Master's graduates in Economics, Finance or a similar field who obtained their degree more than five years ago and are interested in learning the latest developments and trends in the field of Data Science.
  • Managers of a team of data scientists whose role includes overseeing and directing the efforts of the team. 

Fees

* Reduced Fee applies for PhD or Master's students, Alumni of BSE Master's programs, and participants who are unemployed.

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